Structural health monitoring of railroad wheels using wheel impact load detectors

Brant Stratman, Yongming Liu, Sankaran Mahadevan

Research output: Contribution to journalReview article

50 Scopus citations

Abstract

This paper proposes two quantitative criteria for removing railroad wheels from service, based on real-time structural health monitoring trends that are developed using data collected from trains while in service. The data is collected using wheel impact load detectors (WILDs). These impact load trends are able to distinguish wheels with a high probability of failure from high-impact wheels with a low probability of failure. The trends indicate the critical wheels that actually need to be removed, while at the same time allowing wheels that aren't critical to remain in service. As a result, the safety of the railroad will be much improved by being able to identify and remove wheels that have high likelihood of causing catastrophic failures.

Original languageEnglish (US)
Pages (from-to)218-225
Number of pages8
JournalJournal of Failure Analysis and Prevention
Volume7
Issue number3
DOIs
StatePublished - Jun 1 2007

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Keywords

  • Catastrophic failure
  • Crack growth rate
  • Data interpretation
  • Failure analysis
  • Nondestructive testing
  • Structural health monitoring

ASJC Scopus subject areas

  • Materials Science(all)
  • Safety, Risk, Reliability and Quality
  • Mechanics of Materials
  • Mechanical Engineering

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